Robotic lower extremity exoskeleton use in a non-ambulatory child with cerebral palsy: a case study
Bibliographic record
Abstract
PURPOSE: With few treatment options available for non-ambulatory children with cerebral palsy (CP), a robotic lower extremity gait trainer may provide a non-invasive addition to conventional treatment options. This case study investigates the usage and impact of robotic lower extremity gait trainer use in a participant with CP over the initial 3 months of use. MATERIALS AND METHODS: This prospective case study involves a 7-year old female (GMFCS V) with CP (registered clinical trial: NCT04251390). The participant used a Trexo Home robotic gait trainer (Trexo) in the community with assessments occurring in the home and school. Trexo usage and bowel movements (BMs) were tracked daily. Postural control and lower extremity range of motion (ROM) and spasticity were evaluated prior to Trexo use and weekly to biweekly thereafter. RESULTS: The participant used the device an average of 46 min/week, over 3.3 d/week. BM frequency increased from 0.4/d at baseline, to 1.2 (±0.5)/d during Trexo use. There were no diffuse systematic changes in postural stability, ROM or muscle spasticity, but specifically head control and spasticity in the knee flexors had improvements. CONCLUSIONS: Data and anecdotal reports suggest that regular use of the Trexo Home robotic gait trainer has positive outcomes on frequency and quality of BMs, and may improve head control, and knee flexor spasticity. Larger controlled studies are needed to evaluate the impacts of Trexo use in children with CP.Implications for RehabilitationNon-ambulatory children with CP can use and may experience benefits from using a robot-assisted gait trainer (RAGT).Constipation, aspects of balance and focal spasticity may improve.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".